Nightcrab

Excerpt from Jax `_reduction` source: ``` if dtype is None and promote_integers: # Note: NumPy always promotes to 64-bit; jax instead promotes to the # default dtype as defined by...

Looks good to me, just needs a merge.

@martinoywa You might want to use the `arrays_and_axes` helper function to generate the inputs for this test.

Looks to me like a problem with the frontend function. `numpy.matrix.transpose` accepts `axes=None` while `ivy.permute_dims` doesn't, so you need to implement the default behaviour, which according to numpy docs is...

If you call ``ivy.permute_dims(x,[5,4,3,2,1,0])`` where x has 6 dimensions for example, what you get is a reversal of the axes (a.k.a a transposition in 6 dimensions).

It seems like `arrays_and_axes` will not really do what we're looking for which is generating a permutation of size `ndims`. I don't know of a helper that does this, so...

In general there are helpers to randomly generate test cases, but here it would be best to write your own. With the randomly shuffled array, you can provide test inputs...

Regular unit tests aren't randomly generated, and you're still using the hypothesis framework. You'd just be writing your own test strategy.

No need, I'm able to merge the PR into ivy as long as there aren't conflicts.